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A credible traffic prediction method based on self-supervised causal discovery

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Science China(Information Sciences) 2024年第5期67卷 244-258页

作者： Dan WANG Yingjie LIU Bin SONG State Key Laboratory of Integrated services Networks Xidian University

Next-generation wireless network aims to support low-latency, high-speed data transmission services by incorporating artificial intelligence(AI) technologies. To fulfill this promise, AI-based network traffic prediction is essential for pre-allocating resources, such as bandwidth and computing power. This can help reduce network congestion and improve the quality of service(QoS) for users. Most studies achieve future traffic prediction by exploiting deep learning and reinforcement learning, to mine spatio-temporal correlated variables. Nevertheless, the prediction results obtained only by the spatio-temporal correlated variables cannot reflect real traffic changes. This phenomenon prevents the true prediction variables from being inferred, making the prediction algorithm perform poorly. Inspired by causal science, we propose a novel network traffic prediction method based on self-supervised spatio-temporal causal discovery(SSTCD). We first introduce the Granger causal discovery algorithm to build a causal graph among prediction variables and obtain spatio-temporal causality in the observed data, which reflects the real reasons affecting traffic ***, a graph neural network(GNN) is adopted to incorporate causality for traffic prediction. Furthermore,we propose a self-supervised method to implement causal discovery to to address the challenge of lacking ground-truth causal graphs in the observed data. Experimental results demonstrate the effectiveness of the SSTCD method.

关键词： wireless network traffic prediction causal discovery self-supervised

A novel approach towards parametric assessment of reliability and resilience of high voltage mica-based insulation systems by statistical analysis of experimental failure data

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High Voltage 2024年第2期9卷 495-507页

作者： Shahram Negari Davoud Esmaeil Moghadam Integrated Power services ClevelandOhioUSA Valeo eAutomotive Germany GmbH ErlangenGermany

Insulation systems in high-voltage electric machines play a pivotal role in the reliable operation and longevity of the ***-based insulation materials have proven to possess and maintain excellent dielectric properties in the long run and prevent premature insulation *** qualifications tests,such as voltage endurance,are outlined in IEC and IEEE *** authors,however,take a different parametric approach,opting for reliability assessment of insulation systems using derived three-parameter Weibull ***,instead of simple pass–fail criteria,empirical data is employed to determine failure rate probabilities quantitatively and *** data,including breakdown,dissipation factor,and partial discharge mea-surements,are used to construct the Weibull distribution model to predict fault and failure rates and calculate hazard *** rigorous examinations interpreted through the analytical model help assess insulation system resilience and particularly the impact of electrical field stress and mica *** of electrical stress from 66.75 to 71.20 V/mil demonstrated how the mean time to failure of the system changed from 146.4 to 85.1 at 3 Un,hence identifying opportunities for design improvement and uncovering performance ***,the developed framework enhances comprehension of insulation system failure probabilities,guiding design decisions and ensuring a secure and reliable operation of electrical machines across applications.

关键词： insulation parametric approach

Optimization study of spherical tuyere based on BP neural network and new evaluation index

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Building Simulation 2024年第2期17卷 223-234页

作者： Mengchao Liu Ran Gao Yi Wang Angui Li School of Building services Science and Engineering Xi’an University of Architecture and TechnologyXi’an710055China

The energy consumption of heating,ventilation,and air conditioning(HVAC)systems holds a significant position in building energy usage,accounting for about 65%of the total energy ***,with the advancement of building automation,the energy consumption of ventilation systems continues to *** study focuses on improving the performance of spherical tuyeres in HVAC *** primarily utilizes neural networks and multi-island genetic algorithms(MIGA)for multi-parameter *** employing methods such as structural parameterization,accurate and fast computational fluid dynamics(CFD)simulations,a minimized sample space,and a rational optimization strategy,the time cycle of the optimization process is ***,a new comprehensive evaluation index is proposed in this research to describe the performance of spherical tuyeres,which can be used to more accurately assess spherical tuyeres with different *** results show that by establishing a neural network prediction model and combining it with the multi-island genetic algorithm,a novel spherical tuyere design was successfully *** optimized novel spherical tuyeres achieved a 27.05%reduction in the spherical tuyeres effective index(STEI)compared to the traditional spherical ***,the resistance decreased by 15.68%,and the jet length increased by 7.57%.The experimental results demonstrate that our proposed optimization method exhibits high accuracy,good generalization capability,and excellent agreement at different Reynolds numbers.

关键词： spherical tuyeres STEI CFD ventilation

Mitigate noisy data for smart IoT via GAN based machine unlearning

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Science China(Information Sciences) 2024年第3期67卷 62-78页

作者： Zhuo MA Yilong YANG Yang LIU Xinjing LIU Jianfeng MA School of Cyber Engineering Xidian University State Key Laboratory of Integrated services Networks (ISN)

With the development of IoT applications, machine learning dramatically improves the utility of variable IoT systems such as autonomous driving. Although the pretrain-finetune framework can cope well with data heterogeneity in complex IoT scenarios, the data collected by sensors often contain unexpected noisy data, e.g., out-of-distribution(OOD) data, which leads to the reduced performance of fine-tuned models. To resolve the problem, this paper proposes Mu GAN, a method that can mitigate the side-effect of OOD data via the generative adversarial network(GAN)-based machine unlearning. Mu GAN follows a straightforward but effective idea to mitigate the performance loss caused by OOD data, i.e., “flashbacking” the model to the condition where OOD data are excluded from model training. To achieve the goal, we design an adversarial game, where a discriminator is trained to identify whether a sample belongs to the training set by observing the confidence score. Meanwhile, a generator(i.e., the target model) is updated to fool the discriminator into believing that the OOD data are not included in the training set but others do. The experimental results show that benefiting from the high unlearning rate(more than 90%) and retention rate(99%), Mu GAN succeeds in lowering the model performance degradation caused by OOD data from 5.88% to 0.8%.

关键词： machine unlearning generative adversarial network out of distribution data Internet of Thing neural network

Experimental study on the CO_(2) concentration and age of air distribution inside tiny sleeping spaces

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Building Simulation 2024年第4期17卷 591-605页

作者： Jingying Zhang Yanyan Li Haiguo Yin Linfeng Liang Songmei Zu Le Gao Ying Zhang Angui Li School of Building services Science and Engineering Xi’an University of Architecture and TechnologyXi’anShaanxi710055China

In recent years,rapid urban development has led to capsule hotels,sleep pods,and other tiny sleeping spaces that adapt to people’s fast-paced lives,achieving maximum functionality with a very small ***,due to the small space,human metabolic pollutant(such as CO_(2))is more likely to accumulate,and the air is not easily *** this paper,a full-size experimental platform is set up with three types of ventilation modes to explore the exclusion efficiency of metabolic pollutants and the overall distribution of age of air under these ventilation *** conclusions showed that the mean values of metabolic pollutant exclusion rates for the different ventilation modalities varied very little across the spatial dimensions of the confined space but varied considerably in the area around the *** double-side attached ventilation method was the most effective in removing human metabolic pollutants,especially in the head region(CN≥0.92),while the single-wall attached ventilation method had the best air exchange efficiency(η≥0.85).This suggests an inconsistent distribution of CO_(2) and age of air,which is contrary to general common *** conclusions of this paper can guide the design of ventilation for tiny sleeping spaces.

关键词： attached ventilation mode human metabolic pollutant indoor air quality air exchange efficiency tiny sleeping spaces

Analysis of Maximum Liquid Carrying Capacity Based on Conventional Tubing Plunger Gas Lift

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Energy Engineering 2024年第6期121卷 1521-1533页

作者： Yanqun Yu Wenhao Xu Yahui Huangfu Jinhai Liu Bensheng Wang Kai Liu College of Mechanical and Electronic Engineering China University of Petroleum(East China)Qingdao266580China Energy Technology&services Oil Production services Company CNOOCTianjin300450China

China’s unconventional gas fields have a large number of low-productivity and low-efficiency wells, many of whichare located in remote and environmentally harsh mountainous areas. To address the long-term stable productionof these gas wells, plunger-lift technology plays an important role. In order to fully understand and accurately graspthe drainage and gas production mechanisms of plunger-lift, a mechanical model of plunger-liquid column uplift inthe plunger-lift process was established, focusing on conventional plunger-lift systems and representative wellboreconfigurations in the Linxing region. The operating casing pressure of the plunger-lift process and the calculationmethod for the maximum daily fluid production rate based on the work regime with the highest fluid recovery ratewere determined. For the first time, the critical flow rate method was proposed as a constraint for the maximumliquid-carrying capacity of the plunger-lift, and liquid-carrying capacity charts for conventional plunger-lift withdifferent casing sizes were developed. The results showed that for 23/8 casing plunger-lift, with a well depth ofshallower than 808 m, the maximum drainage rate was 33 m3/d;for 27/8 casing plunger-lift, with a well depth ofshallower than 742 m, the maximum drainage rate was 50.15 m3/d;for 31/2 casing plunger-lift, with a well depthof shallower than 560 m, the maximum drainage rate was 75.14 m3/d. This research provides a foundation for thescientific selection of plunger-lift technology and serves as a decision-making reference for developing reasonableplunger-lift work regimes.

关键词： Unconventional well conventional tubing string plunger gas lift critical flow rate liquid carry-over chart version

High-frequency lacustrine sequence stratigraphy of clastic lakes: lessons from ancient successions

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Journal of Palaeogeography 2024年第4期13卷 621-645页

作者： Carlos Zavala Hua-Qing Liu Xiang-Bo Li Valentin Trobbiani Yang Li Mariano Arcuri Agustin Zorzano departamento de Geología Universidad Nacional del Sur (UNS) GCS Consulting services.Bahía Blanca Research Institute of Petroleum Exploration&development-Northwest Consejo Nacional de Investigaciones Científicas y T ecnicas (CONICET) Shandong University of Science and Technology

Sequence stratigraphic concepts and methods provide novel tools for performing stratigraphic analysis, allowing us to improve our understanding of depositional models and basin evolution. Main controls and depositional elements(e.g., surfaces, systems tracts, parasequences, etc.) recognized in conventional sequence stratigraphy are designed for marine-related systems. In contrast, the sequence stratigraphy of lacustrine successions is much more complex and poorly understood, because it is not driven by sea-level changes, but by a complex interaction between tectonics and high-frequency climatic cycles. The comprehensive analysis of the water balance of lacustrine systems allows the recognition of three types of lake conditions: Underfilled, balanced-fill and overfilled lakes. Understanding the lake conditions in ancient successions is fundamental for unraveling lacustrine sequence stratigraphy, since these conditions effectively control water salinity, internal stacking pattern and the characteristics of systems tracts. Underfilled lakes are hydrologically closed lakes, and consequently, the lake-level can highly fluctuate, driven by high-frequency wet-dry climatic cycles. During wet periods, rivers supply water and sediments, resulting in fining-and thinning-upward elementary depositional sequences(EDS's) accumulated during the transgressive systems tract(TST). In contrast, dry periods are characterized by a relative lake-level fall with the subaerial exposure of lake margin areas during the regressive systems tract(RST). Lake water salinity can fluctuate from brackish to hypersaline. Balanced-fill lakes are partially closed lakes, and consequently, they have characteristics of both underfilled and overfilled lakes. During the TST, the lake is in underfilled condition, and consequently, the introduction of water and sediment will accumulate a fining-upward interval until reaching the spill point during the maximum flooding. The RST is accumulated under an overfille

关键词： Lacustrine sequence stratigraphy Underfilled lakes Balanced-fill lakes Overfilled lakes Forced transgression Forced regression

Cognitive Jammers Assisted Covert Communication in Cognitive Radio Networks

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China Communications 2024年第9期21卷 75-89页

作者： Liu Changjun Shi Jia Tie Zhuangzhuang Wang Yongchao The State Key Laboratory of Integrated services Networks Xidian UniversityXi’an 710071China

In this work,we investigate the covert communication in cognitive radio(CR)networks with the existence of multiple cognitive jammers(CJs).Specifically,the secondary transmitter(ST)helps the primary transmitter(PT)to relay information to primary receiver(PR),as a reward,the ST can use PT's spectrum to transmit private information against the eavesdropper(Eve)under the help of one selected cognitive jammer(CJ).Meanwhile,we propose three jammer-selection schemes,namely,link-oriented jammer selection(LJS),min-max jammer selection(MMJS)and random jammer selection(RJS).For each scheme,we analyze the average covert throughput(ACT)and covert outage probability(COP).Our simulation results show that CJ is helpful to ST's covert communication,the expected minimum detection error probability and ACT can be significantly improved with the increase of false alarm of ***,the LJS scheme achieves best performance in ACT and COP,followed by RJS scheme,and MMJS scheme shows the worst performance.

关键词： cognitive jammer cognitive radio covert communication performance analysis

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Soil organic carbon stocks in native forest of Argentina:a useful surrogate for mitigation and conservation planning under climate variability

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Ecological Processes 2024年第1期13卷 3-24页

作者： Pablo L.Peri Juan Gaitán Matías Mastrangelo Marcelo Nosetto Pablo E.Villagra Ezequiel Balducci Martín Pinazo Roxana P.Eclesia Alejandra Von Wallis Sebastián Villarino Francisco Alaggia Marina González Polo Silvina Manrique Pablo A.Meglioli Julián Rodríguez-Souilla Martín Mónaco Jimena E.Chaves Ariel Medina Ignacio Gasparri Eugenio Alvarez Arnesi María Paula Barral Axel von Müller Norberto M.Pahr Josefina Uribe Echevarria Pedro Fernández Marina Morsucci Dardo López Juan Manuel Cellini Leandro Alvarez Ignacio Barberis Hernán Colomb Ludmila La Manna Sebastián Barbaro Cecilia Blundo Ximena Sirimarco Laura Cavallero Gualberto Zalazar Guillermo Martínez Pastur Instituto Nacional de Tecnología Agropecuaria (INTA) Universidad Nacional de la Patagonia Austral (UNPA) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Universidad Nacional de Luján Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP) Universidad Nacional de Mar del Plata Consejo Nacional de Investigaciones Científicas y Técnicas(CONICET ) CCT San Luis Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Instituto Argentino de Nivología Glaciología y Ciencias Ambientales (IANIGLA) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Universidad Nacional de Cuyo Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Instituto de Investigaciones en Energía No Convencional CCT Salta-JujuyConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Laboratorio de Recursos Agroforestales Centro Austral de Investigaciones Científicas (CADIC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Dirección Nacional de Bosques Ministerio de Ambiente y Desarrollo Sostenible de la Nación Instituto de Ecología Regional Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET )Universidad Nacional de Tucumán Universidad Nacional de Rosario Instituto de Investigaciones en Ciencias Agrarias de Rosario Centro Científico Tecnológico CONICET Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP) Universidad Nacional de Mar del Plata Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET ) Laboratorio de Investigaciones en Maderas (LIMAD) Universidad Nacional de La Plata (UNLP) Administración de Parques Nacionales (APN) Parque Nacional Los Alerces Centro de Estudios Ambientales Integrados (CEAI) Universidad Nacional de la Patagonia San Juan Bosco (UNPASJB) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET )

Background The nationally determined contribution（NDC） presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Conven-tion on Climate Change（UNFCCC） on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation（REDD+）. In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon（SOC） stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under cli-mate variability due to a lack of information. The objectives of this study were:（i） to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and（ii） to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter-annual climate *** We used 1040 forest soil samples（0–30 cm） to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covari-ates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud-based computing platform for subsequent modelling. To determine the biodiversity threats from high inter-annual climate variability, we employed the spatial–temporal satellite-derived indices based on Enhanced Vegetation Index（EVI） and land surface temperature（LST） images from Landsat *** SOC model（0–30 cm depth） prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C（2.71–2.84 Pg C with a probability of 90%） for a total area of 460,790 km2,

关键词： Soil organic carbon Biodiversity Native forest Climate change mitigation Modelling Resilience